? DATA ANALYSIS AND MODELING In this Data Analysis and Modeling (DAM) module, we will develop computational methods to derive new knowledge about nuclear compartmentalization and its functional impact. First, we will develop a computational method that combines the distinct types of information provided by genome-wide DamID and TSA-Seq into a unified model that predicts the position and dynamics of genomic regions with respect to various nuclear compartments for the first time. The results will be concrete predictions that will be tested in the Biological Validation Development module using microscopy. Second, we will use computational strategies to predict the mechanisms through which genomic loci are targeted to specific nuclear compartments. Specifically, we will predict which DNA elements and/or epigenetic features are important for this targeting ? yielding testable predictions that will be systematically validated in the Biological Validation Development module, using new technologies developed in the Additional Tool Development or Data Generation module. The results will provide key insights into the mechanisms that drive genome compartmentalization for the first time. Third, we will integrate TRIP, Repli-Seq and other data in order to computationally predict the functional consequences of localization to specific nuclear compartments ? again these predictions will be tested in the Biological Validation Development module. The three aims in this DAM module are tightly coupled to the three aims of the Biological Validation Development module. We envision iterative cycles of collecting data for input to our computational models, making predictions based on these models, and then experimentally testing these predictions. The proposed methods will lead to robust principles and new knowledge of nuclear organization utilizing the new and improved mapping technologies developed in the entire project.